Historically, agencies and entities that undertake or are responsible for winter transportation infrastructure maintenance activities have measured resource utilization in the performance of such winter maintenance activities in several ways. At the lowest level, deicer, abrasive, equipment, and labor usage are estimated and logged by individuals performing winter maintenance, or by their direct supervisors. At the highest level, an agency or entity may also wish to track resource utilization through its procurement and/or payroll processes (e.g. the agency may have direct information as to the quantity of a particular resource it has purchased over a given time frame). In many cases, both approaches are utilized, with ad-hoc methods used to address the potentially substantial differences which inevitably result when trying to integrate data from bottom-up and top-down measurement methods.
In addition to tracking resource utilization, many agencies also define goals for the results of winter maintenance activities, and then attempt to measure the over- or under-achievement of these goals. This may be considered as an attempt to measure the ‘effectiveness’ of winter maintenance operations, and is a far more difficult step due to the inherent subjectivity involved in assessing road conditions, much less the difficulty of tracking changes to the road conditions over time in response to maintenance activities performed in the face of changing weather conditions.
Taken together, knowledge of winter maintenance resource utilization and winter maintenance effectiveness permit the determination of winter maintenance efficiency. There are inherent tradeoffs between resource utilization and the road conditions resulting from it, and efficiency measurement permits evaluation of these tradeoffs.
Complicating an efficiency measurement for winter maintenance activities is the variability of weather conditions. When comparing data from one area to another, or one storm or weather pattern to another, the variability of weather conditions makes it very difficult for winter maintenance managers to know whether the maintenance response was appropriate for the weather conditions being treated. This hinders management's ability to identify which practices and approaches are more effective or efficient, since it is difficult to ascertain whether any particular comparison over time, or between maintenance jurisdictions, is appropriate. Naturally, this impedes the ability of the agency or entity to identify and implement practices and policies which improve winter maintenance effectiveness and efficiency.
The traditional approach to addressing this problem is to develop a winter severity index, which is an attempt to quantify, in a single figure, the impact of varying weather conditions on winter maintenance. There are, however, numerous problems with such an approach to quantification. One problem is that this approach is developed by drawing simplified and often statistical relationships between past weather conditions and historical agency resource utilization. Thus, such indices are typically simply a reflection of an agency's historical response to weather conditions, and thus not a reliable independent metric.
This is reflected in the fact that there are few, if any, instances of an agency successfully adopting and applying a winter severity index developed within another agency. Almost invariably, each agency will change the index, or develop a new index altogether, that better explains the relationship it has historically experienced between weather conditions and maintenance data. This ad-hoc, agency-to-agency approach makes cross-jurisdictional comparisons of maintenance efficiency very difficult. Additionally, the value of winter severity indices is inherently limited because of the gross oversimplification of the underlying relationships. The same weather conditions may elicit an entirely different (yet still appropriate) winter maintenance response depending upon traffic patterns, maintenance policies and resources, and the characteristics of the ambient environment the roads are embedded in. None of these factors can be accounted for by a normalizing metric that is based upon weather alone.